Parallel digital data processing systems are advantageously employed for those computing applications wherein a large amount of data must be analyzed in a relatively short interval of time. By allocating a plurality of data processing elements, such as microprocessors, to simultaneously process the data substantial processing speed improvements are achieved over a traditional single instruction stream, single data stream data processor. Applications where such parallel data processing systems are most beneficial include seismic, meteorologic and imaging applications.
In an imaging application, for example, image pixel data from a large number of sensors may need .to be analyzed in accordance with a preefined algorithm to determine desired features of an object. These features may include the object outline relative to a background, the center of an object and other similar object related criteria. As can be appreciated, if such imaging occurs in a real time manner a large amount of data may need to be processed in an internal of time related to the image acquisition rate, which may be 1/50 or 1/60 of a second if television frame rates are required.
Thus, it can be realized that if a parallel data processing system is to analyze such image pixel data that the physical interconnection between individual PEs of a PA is an important consideration. Inasmuch as image pixel data being analyzed by a given PE may also, depending on the algorithm employed, affect the processing results of other PEs the interchange of image pixel data among PEs must be accomplished in a rapid and efficient manner.
Furthermore, if it is desired to simulate certain properties of neural visual systems the interconnection between PEs may be of critical concern. It is known that individual neurons of the retina and subretinal structures are synaptically coupled not only to adjacent neurons but also to neurons which are spatially removed from a given neuron. These populations of spatially removed neurons may be in the same layer of neural structure or within another layer. This distributed nature of the neural synapse is believed to greatly contribute to the effectiveness of biological visual systems. Thus, any imaging system which simulates such neural visual structures must take into account this feature of distributed synaptic connectivity between neurons in order to accurately model the operation of such visual structures.